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example automated dashboard

library(shiny)
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library(tidyverse)
======= `%>%` <- magrittr::`%>%` plotly_elegant <- function(plot, ..., margin = 3, background = "rgba(20,20,21,1)", color = "#ffffff",size = 16) { plot %>% plotly::layout( xaxis = list(title = ""), yaxis = list(title = ""), font = list(color = color,family = "sans-serif",size = size), plot_bgcolor = background, paper_bgcolor = background, margin = margin ) %>% plotly::layout(...) } dat_tbl <- tidyquant::tq_get("AAPL") write.csv(dat_tbl,"data/dat_tbl.csv") dat_tbl %>% plotly::plot_ly( x = ~date, y = ~adjusted ) %>% plotly::add_lines() %>% plotly_elegant()
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cur_date <- Sys.time()
attr(cur_date,"tzone") <- "UTC"

tags$script(
  shiny::HTML('
 function get_time_diff( datetime ){
  var datetime = new Date(datetime.replace(" ", "T") + "Z").getTime();;
  var now = new Date().getTime();
  var milisec_diff = now - datetime;
  var msec = milisec_diff;
  var hh = Math.floor(msec / 1000 / 60 / 60);
  msec -= hh * 1000 * 60 * 60;
  var mm = Math.floor(msec / 1000 / 60);
  msec -= mm * 1000 * 60;
  var ss = Math.floor(msec / 1000);
  msec -= ss * 1000;
  return hh + " Hours " + mm + " Minutes " + ss + " Seconds";
}
')
)
tags$script(shiny::HTML(glue::glue('
$(function(){{
  $("#date-diff").html("Last Updated " + get_time_diff("{cur_date}") + " ago");    
}});
  
')))
macro_data <- macro_indicators %>%
  dplyr::mutate(data = purrr::map(id,~tidyquant::tq_get(.,get = "economic.data",from = Sys.Date() - 20*365)))

usethis::use_data(macro_data,overwrite = TRUE)


chart_indicators <- function(df,factor_name = NULL){
  df %>%
    filter(factor == factor_name) %>%
    split(.$rowid) %>%
    purrr::map(~ {
      .x %>%
        tidyr::unnest(data) %>%
        highcharter::hchart("line", highcharter::hcaes(date, price)) %>%
        highcharter::hc_title(text = unique(.x$indicator)) %>%
        highcharter::hc_add_theme(highcharter::hc_theme_darkunica())
    }) %>%
    highcharter::hw_grid(ncol = 2)

}

Macro Indicators

Interest Rates

macro_data %>% chart_indicators("Interest Rates")

Inflation

macro_data %>% chart_indicators("Inflation")

Others

macro_data %>% chart_indicators("Others")

Employment

macro_data %>% chart_indicators("Employment")

Housing & construction

macro_data %>% chart_indicators("Housing & construction")

Retail and Consumption

macro_data %>% chart_indicators("Retail and Consumption")

Surveys

macro_data %>% chart_indicators("Surveys")

Manufacturing

macro_data %>% chart_indicators("Manufacturing")

Income

macro_data %>% chart_indicators("Income")

// resize highcharts to fit in main cotent area

document.addEventListener("DOMContentLoaded", function() {

    $(".highchart, .highcharts-container, .highcharts-root").css("max-width","100%");   
   
    var t = setInterval(function(){
      var resizeEvent = new Event('resize');
      window.dispatchEvent(resizeEvent);  
    }, 1000);
 
   
  });
======= $("#date-diff").html("Last Updated " + get_time_diff("2022-02-12 18:07:18") + " ago"); });

Latest Data

dat_tbl %>% tail(1) %>% knitr::kable()
symbol date open high low close volume adjusted
AAPL 2022-02-11 172.33 173.08 168.04 168.64 98566000 168.64
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